Machine Learning-based Perspectives on Climate Change

Speaker: Assaf Shmuel (Weizmann Institute)

Date: 11/11/25

Abstract: Machine learning offers new ways to understand, analyze, and predict climate change. By integrating historical climate records with future projections, it can reveal how climate zones are shifting, how the diurnal temperature cycle is changing, and how species must adapt to emerging conditions. Machine learning approaches also enable earlier detection of climate policy impacts than traditional methods. Such analyses benefit from spatially resolved data rather than global means, since climate responses vary greatly across regions. These tools sharpen predictions, uncover hidden dynamics, and help shape more effective responses to the climate crisis.